Bhusana Premanode

Prediction of nonlinear nonstationary time series data

A Digital Filter and Support Vector Regression

• Broschiertes Buch

Produktbeschreibung

Volatility is a critical parameter when measuring the size of the errors made in modelling returns and other nonlinear nonstationary time series data. The Autoregressive Integrated Moving-Average (ARIMA) model is a linear process in time series; whilst in the nonlinear system, the Generalised Autoregressive Conditional Heteroskedasticity (GARCH) and Markov Switching GARCH (MS-GARCH) models have been widely applied. In statistical learning theory, Support Vector Regression (SVR) plays a significant role in predicting nonlinear and nonstationary time series data. The book contains a new class model comprised a combination of a novel derivative Empirical Mode Decomposition (EMD), averaging intrinsic mode function (aIMF) and a novel of multiclass SVR using mean reversion and coefficient of variance (CV) to predict financial data i.e. EUR-USD exchange rates. The novel aIMF is capable of smoothing and reducing noise, whereas the novel of multiclass SVR model can predict exchange rates.

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Produktdetails

• Verlag: LAP Lambert Academic Publishing
• Seitenzahl: 212
• Erscheinungstermin: 14. Juni 2016
• Englisch
• Abmessung: 220mm x 150mm x 13mm
• Gewicht: 334g
• ISBN-13: 9783659894084
• ISBN-10: 3659894087
• Artikelnr.: 45353966

Autorenporträt

Bhusana has held two Ph.D., DIC from Imperial College London in Electrical Engineering and Biomedical Engineering. He is now working as Visiting Professor at Centre for Bio-Inspired Technology, Imperial College London. Bhusana is the author of 30 papers in the nonlinear system and biomedical science and holds two international patents.